{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<!--BOOK_INFORMATION-->\n",
    "<img align=\"left\" style=\"padding-right:10px;\" src=\"figures/PDSH-cover-small.png\">\n",
    "\n",
    "*This notebook contains an excerpt from the [Python Data Science Handbook](http://shop.oreilly.com/product/0636920034919.do) by Jake VanderPlas; the content is available [on GitHub](https://github.com/jakevdp/PythonDataScienceHandbook).*\n",
    "\n",
    "*The text is released under the [CC-BY-NC-ND license](https://creativecommons.org/licenses/by-nc-nd/3.0/us/legalcode), and code is released under the [MIT license](https://opensource.org/licenses/MIT). If you find this content useful, please consider supporting the work by [buying the book](http://shop.oreilly.com/product/0636920034919.do)!*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<!--NAVIGATION-->\n",
    "< [Feature Engineering](05.04-Feature-Engineering.ipynb) | [Contents](Index.ipynb) | [In Depth: Linear Regression](05.06-Linear-Regression.ipynb) >\n",
    "\n",
    "<a href=\"https://colab.research.google.com/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/05.05-Naive-Bayes.ipynb\"><img align=\"left\" src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open in Colab\" title=\"Open and Execute in Google Colaboratory\"></a>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# In Depth: Naive Bayes Classification"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The previous four sections have given a general overview of the concepts of machine learning.\n",
    "In this section and the ones that follow, we will be taking a closer look at several specific algorithms for supervised and unsupervised learning, starting here with naive Bayes classification.\n",
    "\n",
    "Naive Bayes models are a group of extremely fast and simple classification algorithms that are often suitable for very high-dimensional datasets.\n",
    "Because they are so fast and have so few tunable parameters, they end up being very useful as a quick-and-dirty baseline for a classification problem.\n",
    "This section will focus on an intuitive explanation of how naive Bayes classifiers work, followed by a couple examples of them in action on some datasets."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Bayesian Classification\n",
    "\n",
    "Naive Bayes classifiers are built on Bayesian classification methods.\n",
    "These rely on Bayes's theorem, which is an equation describing the relationship of conditional probabilities of statistical quantities.\n",
    "In Bayesian classification, we're interested in finding the probability of a label given some observed features, which we can write as $P(L~|~{\\rm features})$.\n",
    "Bayes's theorem tells us how to express this in terms of quantities we can compute more directly:\n",
    "\n",
    "$$\n",
    "P(L~|~{\\rm features}) = \\frac{P({\\rm features}~|~L)P(L)}{P({\\rm features})}\n",
    "$$\n",
    "\n",
    "If we are trying to decide between two labels—let's call them $L_1$ and $L_2$—then one way to make this decision is to compute the ratio of the posterior probabilities for each label:\n",
    "\n",
    "$$\n",
    "\\frac{P(L_1~|~{\\rm features})}{P(L_2~|~{\\rm features})} = \\frac{P({\\rm features}~|~L_1)}{P({\\rm features}~|~L_2)}\\frac{P(L_1)}{P(L_2)}\n",
    "$$\n",
    "\n",
    "All we need now is some model by which we can compute $P({\\rm features}~|~L_i)$ for each label.\n",
    "Such a model is called a *generative model* because it specifies the hypothetical random process that generates the data.\n",
    "Specifying this generative model for each label is the main piece of the training of such a Bayesian classifier.\n",
    "The general version of such a training step is a very difficult task, but we can make it simpler through the use of some simplifying assumptions about the form of this model.\n",
    "\n",
    "This is where the \"naive\" in \"naive Bayes\" comes in: if we make very naive assumptions about the generative model for each label, we can find a rough approximation of the generative model for each class, and then proceed with the Bayesian classification.\n",
    "Different types of naive Bayes classifiers rest on different naive assumptions about the data, and we will examine a few of these in the following sections.\n",
    "\n",
    "We begin with the standard imports:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns; sns.set()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Gaussian Naive Bayes\n",
    "\n",
    "Perhaps the easiest naive Bayes classifier to understand is Gaussian naive Bayes.\n",
    "In this classifier, the assumption is that *data from each label is drawn from a simple Gaussian distribution*.\n",
    "Imagine that you have the following data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.datasets import make_blobs\n",
    "X, y = make_blobs(100, 2, centers=2, random_state=2, cluster_std=1.5)\n",
    "plt.scatter(X[:, 0], X[:, 1], c=y, s=50, cmap='RdBu');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "One extremely fast way to create a simple model is to assume that the data is described by a Gaussian distribution with no covariance between dimensions.\n",
    "This model can be fit by simply finding the mean and standard deviation of the points within each label, which is all you need to define such a distribution.\n",
    "The result of this naive Gaussian assumption is shown in the following figure:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![(run code in Appendix to generate image)](figures/05.05-gaussian-NB.png)\n",
    "[figure source in Appendix](06.00-Figure-Code.ipynb#Gaussian-Naive-Bayes)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "The ellipses here represent the Gaussian generative model for each label, with larger probability toward the center of the ellipses.\n",
    "With this generative model in place for each class, we have a simple recipe to compute the likelihood $P({\\rm features}~|~L_1)$ for any data point, and thus we can quickly compute the posterior ratio and determine which label is the most probable for a given point.\n",
    "\n",
    "This procedure is implemented in Scikit-Learn's ``sklearn.naive_bayes.GaussianNB`` estimator:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.naive_bayes import GaussianNB\n",
    "model = GaussianNB()\n",
    "model.fit(X, y);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's generate some new data and predict the label:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "rng = np.random.RandomState(0)\n",
    "Xnew = [-6, -14] + [14, 18] * rng.rand(2000, 2)\n",
    "ynew = model.predict(Xnew)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we can plot this new data to get an idea of where the decision boundary is:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(X[:, 0], X[:, 1], c=y, s=50, cmap='RdBu')\n",
    "lim = plt.axis()\n",
    "plt.scatter(Xnew[:, 0], Xnew[:, 1], c=ynew, s=20, cmap='RdBu', alpha=0.1)\n",
    "plt.axis(lim);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We see a slightly curved boundary in the classifications—in general, the boundary in Gaussian naive Bayes is quadratic.\n",
    "\n",
    "A nice piece of this Bayesian formalism is that it naturally allows for probabilistic classification, which we can compute using the ``predict_proba`` method:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.89, 0.11],\n",
       "       [1.  , 0.  ],\n",
       "       [1.  , 0.  ],\n",
       "       [1.  , 0.  ],\n",
       "       [1.  , 0.  ],\n",
       "       [1.  , 0.  ],\n",
       "       [0.  , 1.  ],\n",
       "       [0.15, 0.85]])"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "yprob = model.predict_proba(Xnew)\n",
    "yprob[-8:].round(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The columns give the posterior probabilities of the first and second label, respectively.\n",
    "If you are looking for estimates of uncertainty in your classification, Bayesian approaches like this can be a useful approach.\n",
    "\n",
    "Of course, the final classification will only be as good as the model assumptions that lead to it, which is why Gaussian naive Bayes often does not produce very good results.\n",
    "Still, in many cases—especially as the number of features becomes large—this assumption is not detrimental enough to prevent Gaussian naive Bayes from being a useful method."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Multinomial Naive Bayes\n",
    "\n",
    "The Gaussian assumption just described is by no means the only simple assumption that could be used to specify the generative distribution for each label.\n",
    "Another useful example is multinomial naive Bayes, where the features are assumed to be generated from a simple multinomial distribution.\n",
    "The multinomial distribution describes the probability of observing counts among a number of categories, and thus multinomial naive Bayes is most appropriate for features that represent counts or count rates.\n",
    "\n",
    "The idea is precisely the same as before, except that instead of modeling the data distribution with the best-fit Gaussian, we model the data distribuiton with a best-fit multinomial distribution."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Example: Classifying Text\n",
    "\n",
    "One place where multinomial naive Bayes is often used is in text classification, where the features are related to word counts or frequencies within the documents to be classified.\n",
    "We discussed the extraction of such features from text in [Feature Engineering](05.04-Feature-Engineering.ipynb); here we will use the sparse word count features from the 20 Newsgroups corpus to show how we might classify these short documents into categories.\n",
    "\n",
    "Let's download the data and take a look at the target names:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "sklearn.utils.Bunch"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.datasets import fetch_20newsgroups\n",
    "\n",
    "data = fetch_20newsgroups()\n",
    "data.target_names\n",
    "type(data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For simplicity here, we will select just a few of these categories, and download the training and testing set:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "str"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "categories = ['talk.religion.misc', 'soc.religion.christian',\n",
    "              'sci.space', 'comp.graphics']\n",
    "train = fetch_20newsgroups(subset='train', categories=categories)\n",
    "test = fetch_20newsgroups(subset='test', categories=categories)\n",
    "type(train)\n",
    "type(train.data[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here is a representative entry from the data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "From: dmcgee@uluhe.soest.hawaii.edu (Don McGee)\n",
      "Subject: Federal Hearing\n",
      "Originator: dmcgee@uluhe\n",
      "Organization: School of Ocean and Earth Science and Technology\n",
      "Distribution: usa\n",
      "Lines: 10\n",
      "\n",
      "\n",
      "Fact or rumor....?  Madalyn Murray O'Hare an atheist who eliminated the\n",
      "use of the bible reading and prayer in public schools 15 years ago is now\n",
      "going to appear before the FCC with a petition to stop the reading of the\n",
      "Gospel on the airways of America.  And she is also campaigning to remove\n",
      "Christmas programs, songs, etc from the public schools.  If it is true\n",
      "then mail to Federal Communications Commission 1919 H Street Washington DC\n",
      "20054 expressing your opposition to her request.  Reference Petition number\n",
      "\n",
      "2493.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(train.data[5])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In order to use this data for machine learning, we need to be able to convert the content of each string into a vector of numbers.\n",
    "For this we will use the TF-IDF vectorizer (discussed in [Feature Engineering](05.04-Feature-Engineering.ipynb)), and create a pipeline that attaches it to a multinomial naive Bayes classifier:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "sklearn.feature_extraction.text.TfidfVectorizer"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "from sklearn.naive_bayes import MultinomialNB\n",
    "from sklearn.pipeline import make_pipeline\n",
    "\n",
    "model = make_pipeline(TfidfVectorizer(), MultinomialNB())\n",
    "type(TfidfVectorizer(train.data))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With this pipeline, we can apply the model to the training data, and predict labels for the test data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.fit(train.data, train.target)\n",
    "labels = model.predict(test.data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that we have predicted the labels for the test data, we can evaluate them to learn about the performance of the estimator.\n",
    "For example, here is the confusion matrix between the true and predicted labels for the test data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "mat = confusion_matrix(test.target, labels)\n",
    "sns.heatmap(mat.T, square=True, annot=True, fmt='d', cbar=False,\n",
    "            xticklabels=train.target_names, yticklabels=train.target_names)\n",
    "plt.xlabel('true label')\n",
    "plt.ylabel('predicted label');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Evidently, even this very simple classifier can successfully separate space talk from computer talk, but it gets confused between talk about religion and talk about Christianity.\n",
    "This is perhaps an expected area of confusion!\n",
    "\n",
    "The very cool thing here is that we now have the tools to determine the category for *any* string, using the ``predict()`` method of this pipeline.\n",
    "Here's a quick utility function that will return the prediction for a single string:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "def predict_category(s, train=train, model=model):\n",
    "    pred = model.predict([s])\n",
    "    return train.target_names[pred[0]]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's try it out:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'sci.space'"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predict_category('sending a payload to the ISS')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'soc.religion.christian'"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predict_category('discussing islam vs atheism')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'comp.graphics'"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predict_category('determining the screen resolution')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Remember that this is nothing more sophisticated than a simple probability model for the (weighted) frequency of each word in the string; nevertheless, the result is striking.\n",
    "Even a very naive algorithm, when used carefully and trained on a large set of high-dimensional data, can be surprisingly effective."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## When to Use Naive Bayes\n",
    "\n",
    "Because naive Bayesian classifiers make such stringent assumptions about data, they will generally not perform as well as a more complicated model.\n",
    "That said, they have several advantages:\n",
    "\n",
    "- They are extremely fast for both training and prediction\n",
    "- They provide straightforward probabilistic prediction\n",
    "- They are often very easily interpretable\n",
    "- They have very few (if any) tunable parameters\n",
    "\n",
    "These advantages mean a naive Bayesian classifier is often a good choice as an initial baseline classification.\n",
    "If it performs suitably, then congratulations: you have a very fast, very interpretable classifier for your problem.\n",
    "If it does not perform well, then you can begin exploring more sophisticated models, with some baseline knowledge of how well they should perform.\n",
    "\n",
    "Naive Bayes classifiers tend to perform especially well in one of the following situations:\n",
    "\n",
    "- When the naive assumptions actually match the data (very rare in practice)\n",
    "- For very well-separated categories, when model complexity is less important\n",
    "- For very high-dimensional data, when model complexity is less important\n",
    "\n",
    "The last two points seem distinct, but they actually are related: as the dimension of a dataset grows, it is much less likely for any two points to be found close together (after all, they must be close in *every single dimension* to be close overall).\n",
    "This means that clusters in high dimensions tend to be more separated, on average, than clusters in low dimensions, assuming the new dimensions actually add information.\n",
    "For this reason, simplistic classifiers like naive Bayes tend to work as well or better than more complicated classifiers as the dimensionality grows: once you have enough data, even a simple model can be very powerful."
   ]
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    "<!--NAVIGATION-->\n",
    "< [Feature Engineering](05.04-Feature-Engineering.ipynb) | [Contents](Index.ipynb) | [In Depth: Linear Regression](05.06-Linear-Regression.ipynb) >\n",
    "\n",
    "<a href=\"https://colab.research.google.com/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/05.05-Naive-Bayes.ipynb\"><img align=\"left\" src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open in Colab\" title=\"Open and Execute in Google Colaboratory\"></a>\n"
   ]
  }
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